Creating Publication-Quality Graphics with ggplot2

2021-06-16

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Workshop Overview

Teaching: 60 minutes
Exercises: 20 minutes

Guiding Questions:

What are the components of a ggplot?
How do I create scatterplots, boxplots, and barplots?
How can I change the aesthetics (ex. colour, transparency) of my plot?
How can I create multiple plots at once?

Lesson Objectives:

Produce scatter plots, boxplots, and barplots using ggplot.
Set universal plot settings.
Describe what faceting is and apply faceting in ggplot.
Modify the aesthetics of an existing ggplot plot (including axis labels and colour).
Build complex and customized plots from data in a data frame.

We start by loading the required package. ggplot2 is also included in the tidyverse package.

library(tidyverse)

If not still in the workspace, load the data we saved in the previous lesson.

gapminder <- read_csv(
  here::here("data", "gapminder_data.csv")
)

Warm-up

Describe the qualities of this plot.




Data Visualization using ggplot2

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatter plot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like str() or mean(), come built into R; packages give you access to more of them. Before you use a package for the first time you need to install it on your machine, and then you should import it in every subsequent R session when you need it. You should already have installed the tidyverse package. This is an “umbrella-package” that installs several packages useful for data analysis which work together well such as tidyr, dplyr, ggplot2, readr, forcats, etc.

The tidyverse package tries to address common issues that arise when doing data analysis with some of the functions that come with R.

  1. The tidyverse solves complex problems by combining many simple pieces.

No matter how complex and polished the individual operations are, it is often the quality of the glue that most directly determines the power of the system. - Hal Abelson

  1. The tidyverse is written for people to read!

Computer efficiency is a secondary concern because the bottleneck in most data analysis is thinking time, not computing time. - Hadley Wickham

If we haven’t already done so, we can type install.packages("tidyverse") straight into the console. In fact, it’s better to write this in the console than in our script for any package, as there’s no need to re-install packages every time we run the script.

Then, to load the package type:

## load the tidyverse packages
library(tidyverse)

To learn more about ggplot2 after the workshop, you may want to check out this ggplot2 reference website (link) and this handy cheatsheet on ggplot2 (link).

gapminder data

We’ll read in the gapminder data from yesterday using the read_csv() function and the here() function.

gapminder <- read_csv(
  here::here("data", "gapminder_data.csv")
)

Plotting with ggplot2

ggplot2 functions like data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. There are other data formats, which we will discuss in the Data Wrangling in R workshop, as well as how to covert from one data format to another. Well-structured data will save you lots of time when making figures with ggplot2 and when working in R!

ggplot() graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a ggplot(), we will use the following basic template that can be used for different types of plots:

ggplot(data = <DATA>, mapping = aes(<VARIABLE MAPPINGS>)) +  
<GEOM_FUNCTION>()


Let’s go through this step by step!

  1. Use the ggplot() function and bind the plot to a specific data frame using the data argument
ggplot(data = gapminder) 

## Creates a blank ggplot(), referencing the gapminder dataset
  1. Define a mapping (using the aesthetic (aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp))

## Creates a blank ggplot(), with the variables mapped to the x- and y-axis
## ggplot() knows where the variables live, since you have defined the data to use

  1. Add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones today, including:

    • geom_point() for scatter plots
    • geom_boxplot() for boxplots
    • geom_bar() for bar charts
    • geom_line() for trend lines, time series, etc.

Because we have two continuous variables in the data, let’s use geom_point() first:

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

 ## Adds a point for each row (observation) in the data

You can think of the + sign as adding layers to the plot. Each + sign must be placed at the end of the line containing the previous layer. If, instead, the + sign is added at the beginning of the line containing the new layer, ggplot2 will not add the new layer and will return an error message.

# This is the correct syntax for adding layers
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

# This will not add the new layer and will return an error message
ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) 

+ geom_point()
Error: Cannot use `+.gg()` with a single argument. Did you accidentally put + on a new line?

Building Plots Iteratively

Building plots with ggplot2 is typically an iterative process. We start by defining the dataset we’ll use, lay out the axes, and choose a geom:

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Then, we start modifying this plot to extract more information from it. For instance, we can add transparency (alpha) to the points, to avoid overplotting:

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.2)

    ## alpha reduces the opacity of the points 
    ## 0 is fully transparent
    ## 1 is the original opacity

We can also add colors for all the points:

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.1, color = "blue")

Your turn!

Challenge 1

geom_point also accepts aesthetics of size and shape. The size of a point is its width in mm. The shape of a point has five different options for plotting:

  • an integer [0, 25] of defined plotting characters – same as base R
  • the name of the shape in quotations (e.g. “circle open” or “diamond filled”)
  • a single character, to use that character as a plotting symbol
  • a “.” to draw the smallest point that is visible – typically 1 pixel
  • an NA, to draw nothing

Reference for shapes in integers and characters:
https://ggplot2.tidyverse.org/articles/ggplot2-specs.html

Modify the previous code chunk to assign one of these aesthetics to the geom_point aspect of your plot. What happened?

## Your ggplot code to answer the challenge goes here!

Assigning More Variables to Aesthetics

To color each continent in the plot differently, you could use a vector as an input to the argument color. ggplot2 will provide a different color corresponding to different values in the vector. Here is an example where we color with continent:

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.1,  aes(color = continent))

Note: When specifying an alpha for a scatterplot, it automatically uses that same alpha in the legend. To remedy this you can add:

guides(colour = guide_legend(override.aes = list(alpha = 1)))

to your plot. This customizes the legend appearance, similar to what we will see in the customization section.



We can also specify the colors directly inside the mapping provided in the ggplot() function. This will be seen by any geom layers and the mapping will be determined by the x- and y-axis set up in aes().

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(alpha = 0.2)

Notice that we can change the geom layer and colors will be still determined by continent

Local Aesthetics versus Global Aesthetics

When you define aesthetics in the ggplot() function, those mappings hold for every aspect of your plot.

For example, if you chose to add a trend line to your plot of GDP per cap versus life expectancy, you would get different lines depending on where you define your color aesthetics.

Globally

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(alpha = 0.2) + 
  geom_smooth()

  ## trend line for each continent -- because color is defined globally

Locally

ggplot(data = gapminder, 
       mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.2, aes(color = continent)) + 
  geom_smooth()

  ## one trend line -- no color defined globally

Challenge 2

Part 1: Inspect the geom_point help file to see what other aesthetics are available. Map a new variable from the dataset to another aesthetic in your plot. What happened? Does the aesthetic change if you use a continuous variable versus a categorical/discrete variable?

## Your ggplot() code for the challenge goes here!

Part 2: Use what you just learned to create a scatter plot of lifeExp over time with data from different continents being showed in different colors. Is this a good way to show this type of data?

## Your ggplot() code for the challenge goes here!

Layers

Using a scatterplot probably isn’t the best for visualizing change over time. Instead, let’s tell ggplot to visualize the data as a line plot:

ggplot(data = gapminder, 
       mapping = aes(x = year, y = lifeExp, group = country, color = continent)) +
  geom_line()

Instead of adding a geom_point() layer, we’ve added a geom_line() layer. We’ve added the group aesthetic, which tells ggplot to draw a line for each country. Without the group aesthetic, our plot would look something like:

ggplot(data = gapminder, 
       mapping = aes(x = year, y = lifeExp, color = continent)) +
  geom_line()

Multiple geoms

But what if we want to visualize both lines and points on the plot? We can add another layer to our line plot!

ggplot(data = gapminder, 
       mapping = aes(x = year, y = lifeExp, group = country, color = continent)) +
  geom_line() + 
  geom_point()

It’s important to note that each layer is drawn on top of the previous layer. In this example, the points have been drawn on top of the lines. Here’s a demonstration:

ggplot(data = gapminder, 
  mapping = aes(x = year, y = lifeExp, group = country)) +
  geom_line(mapping = aes(color = continent)) + 
  geom_point()

In this example, the aesthetic mapping of color has been moved from the global plot options in ggplot to the geom_line layer so it no longer applies to the points. Now we can clearly see that the points are drawn on top of the lines.

Challenge 3

Switch the order of the point and line layers from the previous example. What happened?

Boxplots & Violin Plots

Another option to plotting the relationship between life expectancy and time is to consider year as a categorical variable. In this case, we can use boxplots to visualize how the distribution of life expectancy changes over time.

Let’s give this a try:

ggplot(data = gapminder, 
  mapping = aes(x = year, y = lifeExp)) +
  geom_boxplot()

Oh no! We forgot that year is a numerical variable, so geom_boxplot() is plotting the boxplot at the center of the values of year. To make the plot we wanted, we need to first create a year variable that is a character or factor.

gapminder$year_chr <- as.character(gapminder$year)

Now, let’s try this again!

By adding points to boxplot, we can have a better idea of the number of measurements and their distribution:

ggplot(data = gapminder, 
  mapping = aes(x = year_chr, y = lifeExp)) +
  geom_boxplot(outlier.alpha = NA) +   
  ## outlier.alpha eliminates the black (outlier) points, so they're not plotted twice
  geom_jitter(alpha = 0.2, color = "tomato")

Notice how the boxplot layer is behind the jitter layer? What would you change in the code to put the boxplot in front of the points?

Challenge 4

Part 1: Boxplots are useful summaries, but hide the shape of the distribution. For example, if the distribution is bimodal, we would not see it in a boxplot. A superior density plot is the violin plot, where the shape (of the density of points) is drawn.

Replace the box plot with a violin plot. For help see geom_violin().

##  Start with the boxplot we created:
ggplot(data = gapminder, 
  mapping = aes(x = year_chr, y = lifeExp)) +
  geom_boxplot(outlier.alpha = NA) +   
  geom_jitter(alpha = 0.2, color = "tomato")

##  1. Replace the boxplot with a violin plot. For help, see geom_violin().

Faceting

Earlier we visualized the change in life expectancy over time across all countries in one plot. Alternatively, we can split this out over multiple panels by adding a layer of facet panels.

We start by making a subset of data including only countries located in the Americas. This includes 25 countries, which will begin to clutter the figure. Note that we apply a “theme” definition to rotate the x-axis labels to maintain readability. Nearly everything in ggplot2 is customizable.

americas <- gapminder %>% 
  filter(continent == "Americas") 

americas %>% 
ggplot(mapping = aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap( ~ country) +
  theme(axis.text.x = element_text(angle = 45))

The facet_wrap layer took a “formula” as its argument, denoted by the tilde (~). This tells R to draw a panel for each unique value in the country column of the gapminder dataset.

Modifying text

To clean this figure up for a publication we need to change some of the text elements. The x-axis is too cluttered, and the y axis should read “Life expectancy”, rather than the column name in the data frame.

We can do this by adding a couple of different layers. The theme layer controls the axis text, and overall text size. Labels for the axes, plot title and any legend can be set using the labs function. Legend titles are set using the same names we used in the aes specification. Thus below the color legend title is set using color = "Continent", while the title of a fill legend would be set using fill = "MyTitle".

ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap( ~ country) +
  theme(axis.text.x = element_text(angle = 45)) + 
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  )